Early life nutrition in Nunavut, Canada: a retrospective descriptive study of food security, vitamin D and rickets
Bibliographic record
Abstract
Northern regions of Canada have the highest reported incidence of childhood rickets in the country, yet this public health problem remains poorly described. The goal of this research was to explore the food and vitamin D supplementation experiences in pregnancy and infancy and examine associations with rickets diagnosis. Data were collected systematically through a retrospective chart review of Inuit children from 18 communities in Nunavut born from 2010-2013. Although most pregnant people reported consuming country food daily or weekly, one in three pregnant people reported being food insecure. Fewer than half of infants were reported to have received daily vitamin D supplement at two months of age, and frequency of supplement use declined with age. Rickets diagnosis was present in 1.63% of children (95% CI = 1.20%-2.20%). The odds of rickets diagnosis were higher for children whose mothers experienced food insecurity during pregnancy than for those whose mothers had never experienced food insecurity (OR = 5.279; 95% CI = 1.248-16.191). Enhanced support for food security, breastfeeding and vitamin D supplementation in early life is needed. In the context of social determinants of health, this study highlights the far-reaching and negative impacts of food insecurity on the health of Inuit children in Nunavut.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".